1. Field of the Invention
The present invention relates to improvements in or relating to signal analysis, in particular to a method for the analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR).
2. Description of the Related Art
Despite improvements in the rapidity with which shocks are delivered and the shock characteristics themselves, results achieved from the treatment of cardiac arrest remain sub-optimal. For the past two decades, therefore, efforts have been made to characterise the ECG waveform during cardiac arrest in an attempt to optimise shock delivery and outcome.
Experimental [1,2,3] and clinical [4] studies have indicated that administering Cardiopulmonary Resuscitation (CPR) prior to shock therapy can increase the likelihood of successful defibrillation for established ventricular fibrillation (VF). Further studies have suggested that delaying CPR for a defibrillation attempt may cause a dramatic decrease in the likelihood of defibrillation success [5]. These results are consistent with clinical studies [6,7] which indicate that pre-shock CPR can improve the rates of return of spontaneous circulation (ROSC) and survival to hospital discharge when emergency medical services (EMS) response times exceeded 4-5 minutes.
There is therefore a shift in emphasis in resuscitation protocols with uninterrupted CPR taking a more prevalent roll. Specifically, there is recognition that, while defibrillation is the only effective means of reverting the heart to normal sinus rhythm, maximising the quantity and quality of CPR and reducing the ‘hands-free’ periods where CPR is not delivered has a substantial bearing on the likely efficacy of defibrillation therapy.
In current resuscitation protocols CPR is regularly halted for a period of time (up to 20 or 30 seconds) in order to identify whether the patient should receive a defibrillation shock, i.e. whether the underlying myocardial rhythm is of a type that is shockable such as VF or ventricular tachycardia (VT) or non-shockable such as Asystole or pulseless electrical activity (PEA). It has been shown recently that this cessation of CPR, during which the ECG trace is analysed, can inhibit the effectiveness of any subsequent defibrillation attempt [8].
Accordingly there is a need for a technique which can effectively analyse the underlying cardiac signal during CPR. Adaptive filters have been used by others in an attempt to filter the ECG of CPR artefact using available secondary signals (e.g. accelerometer). However, these methods typically involve digital filters with coefficients that have evolved from averaged historic data (Wiener-like) [9] or linearly scaled super-positioning of reference data, and often tap-delayed reference data when the system is assumed to be non-causal, for best signal approximation (Matching pursuit-like) [10].
The former (Wiener-like) is highly likely to have considerable residuals due to the non-stationary nature of the artefact signal. This will necessarily leave artefact in the de-noised trace making rhythm identification difficult. This is particularly the case for asystole. The latter (Matching pursuit-like) will have either residual artefact components if too few iterations of the MP algorithm are executed or it will lose components of the underlying myocardium rhythm if too many approximation iterations are applied. Often the practical identification of this ‘depth of recursion’ parameter is not fully discussed in published literature.